Constrained random simulation coverage closure guided by a cover property

ABSTRACT

One embodiment of the present invention provides a system which verifies a circuit design by biasing input stimuli for the circuit design to satisfy one or more temporal coverage properties to be verified for the circuit design. This system performs a simulation in which random input stimuli are applied to the circuit design. The system performs the simulation by using a finite state automaton (FSA) instance for a temporal coverage property to observe inputs and outputs of the circuit, and by using soft constraints associated with the FSA instance to bias the input stimuli for the circuit design so that the simulation is likely to progress through a sequence of states which satisfy the temporal coverage property.

BACKGROUND

1. Field of the Invention

The present invention generally relates to techniques for verifying thecorrectness of a circuit design. More specifically, the presentinvention relates to an input vector generation technique for achievingcoverage closure which biases random input stimuli based on a temporalcoverage property.

2. Related Art

Advances in semiconductor fabrication technology have given rise todramatic increases in the number of transistors per semiconductordevice. This increase in transistor count is empowering computerarchitects to create digital circuit designs with an ever-increasingdesign complexity. Consequently, as digital circuit designs become morecomplex, the effort required to verify the correctness of theirimplementation also becomes more involved.

To verify the functionality of a circuit design, circuit designverification teams typically apply random input stimuli onto a circuitdesign under verification (DUV) to stimulate the DUV and compare theresponse from simulation to the expected response. Simulating the DUVagainst random input stimuli is a stochastic process which relies on ahigh volume of input vectors to achieve a reasonable coverage of thebehavior of the DUV. However, as the circuit designs become morecomplex, the random input stimuli become less effective at covering theimportant corner cases of the DUV.

Circuit design verification teams have attempted to guide the testselection process to effectively cover the important corner cases of theDUV. In doing so, they have attempted to employ methods which includeusing directives from the designer, non-covered bins in SystemVerilogcovergroups as supplementary constraints, genetic algorithms,user-supplied additional constraints, and Bayesian networks or Markovchains. Unfortunately, none of these approaches provides a complete andautomatic solution for biasing the random input stimuli toward achievingcoverage closure.

Hence what is needed is a more effective technique for guiding inputstimuli toward the important cases of a circuit design underverification.

SUMMARY

One embodiment of the present invention provides a system which verifiesa circuit design by biasing input stimuli for the circuit design tosatisfy one or more temporal coverage properties to be verified for thecircuit design. During operation, this system performs a simulation inwhich random input stimuli are applied to the circuit design. The systemperforms this simulation by using a finite state automaton (FSA)instance for a temporal coverage property to observe inputs and outputsof the circuit, and by using soft constraints associated with the FSAinstance to bias the input stimuli for the circuit design so that thesimulation is likely to progress through a sequence of states whichsatisfy the temporal coverage property.

In a variation on this embodiment, the system uses the soft constraintsto bias the input stimuli based on a current state of the FSA instance.

In a variation on this embodiment, if the FSA instance reaches anaccepting state for the temporal coverage property, the system marks thetemporal coverage property as satisfied.

In a variation on this embodiment, the system uses the FSA instance toobserve the inputs and outputs of the circuit by concurrently usingmultiple FSA instances associated with one or more temporal coverageproperties to observe the inputs and outputs of the circuit.

In a variation on this embodiment, the system biases the input stimulifor the circuit by ensuring that FSA instances which are closer to anaccepting state have a higher likelihood of biasing the input stimulithan FSA instances which are farther from an accepting state.

In a variation on this embodiment, prior to performing the simulation,the system performs a preceding non-biased simulation, in which theinput stimuli to the circuit are not biased to satisfy specific temporalcoverage properties. After the preceding non-biased simulation iscomplete, the simulation subsequently biases the input stimuli to covertemporal coverage properties which were not covered during thenon-biased simulation.

In a variation on this embodiment, prior to performing the simulation,the method further comprises converting the temporal coverage propertyinto the FSA and the soft constraints.

In a variation on this embodiment, the FSA is a deterministic FSA.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a circuit verification environment in accordance withan embodiment of the present invention.

FIG. 2 illustrates a circuit verification system in accordance with anembodiment of the present invention.

FIG. 3 illustrates an exemplary usage of a conversion mechanism inaccordance with an embodiment of the present invention.

FIG. 4 presents a flowchart illustrating the steps involved ingenerating input vectors to achieve coverage closure in accordance withan embodiment of the present invention.

FIG. 5 presents a flowchart illustrating the steps involved ingenerating input vectors for a temporal coverage property in accordancewith an embodiment of the present invention.

FIG. 6 presents a flowchart illustrating the steps involved ingenerating random inputs for a subsequent time frame in accordance withan embodiment of the present invention.

FIG. 7 presents a flowchart illustrating the steps involved in advancingthe simulation time in accordance with an embodiment of the presentinvention.

FIG. 8 presents a flowchart illustrating the steps involved in pruningthe FSA instance collection in accordance with an embodiment of thepresent invention.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the invention, and is provided in the context ofa particular application and its requirements. Various modifications tothe disclosed embodiments will be readily apparent to those skilled inthe art, and the general principles defined herein may be applied toother embodiments and applications without departing from the spirit andscope of the present invention. Thus, the present invention is notlimited to the embodiments shown, but is to be accorded the widest scopeconsistent with the claims.

The data structures and code described in this detailed description aretypically stored on a computer-readable storage medium, which may be anydevice or medium that can store code and/or data for use by a computersystem. This includes, but is not limited to, volatile memory,non-volatile memory, magnetic and optical storage devices such as diskdrives, magnetic tape, CDs (compact discs), DVDs (digital versatilediscs or digital video discs), or other media capable of storingcomputer-readable media now known or later developed.

Circuit Verification Environment

FIG. 1 illustrates circuit verification environment 100 in accordancewith an embodiment of the present invention. Circuit verificationenvironment 100 includes a number of computer systems, which cangenerally include any type of computer system based on a microprocessor,a mainframe computer, a digital signal processor, a portable computingdevice, a personal organizer, a device controller, or a computationalengine within an appliance. More specifically, referring to FIG. 1, thecircuit verification environment 100 includes workstation 102, user 104,network 106, and circuit simulator 108.

Network 106 can include any type of wired or wireless communicationchannel capable of coupling together computer nodes. This includes, butis not limited to, a local area network, a wide area network, or acombination of networks. In one embodiment of the present invention,network 106 includes the Internet. In some embodiments of the presentinvention, network 106 includes phone and cellular phone networks.

Workstation 102 can generally include any device including computationalcapability and including a mechanism for interacting with user 104.Workstation 102 includes operating system 110, monitoring mechanism 112,testbench 114, conversion mechanism 116, temporal coverage propertydatabase 118, and circuit design under verification 120.

User 104 can include an individual, a group of individuals, anorganization, a group of organizations, a computing system, a group ofcomputing systems, or any other entity which can interact with thecircuit verification environment 100.

Circuit simulator 108 can generally include any device includingcomputational capability for performing a circuit simulation, includingany type of computer system based on a microprocessor, a mainframecomputer, a digital signal processor, a portable computing device, adevice controller, a field-programmable gate array (FPGA), or a group ofFPGAs.

Note that different embodiments of the present invention may usedifferent configurations, and are not limited to the configurationillustrated in circuit verification environment 100. In some embodimentsof the present invention, workstation 102 is coupled to circuitsimulator 108 through network 106, while in other embodiments of thepresent invention, workstation 102 is coupled directly to circuitsimulator 108. In yet another embodiment of the present invention,workstation 102 includes circuit simulator 108.

FIG. 2 illustrates circuit verification system 200 in accordance with anembodiment of the present invention. Circuit verification system 200includes temporal coverage property database 204, conversion mechanism206, testbench 212, design under verification (DUV) 214, and monitoringmechanism 216.

Temporal coverage property database 204 can include any type of systemfor storing data in non-volatile storage. This includes, but is notlimited to, systems based upon magnetic, optical, or magneto-opticalstorage devices, as well as storage devices based on flash memory and/orbattery-backed up memory. Note that temporal coverage property database204 can be coupled to a server, to a client (such as workstation 102 ofFIG. 1), or directly to a network. Temporal coverage property database204 holds a collection of temporal coverage properties 202, whichtogether make up the verification coverage plan for DUV 214.

During runtime, conversion mechanism 206 accepts one or more temporalcoverage property 202 as input and generates a corresponding finitestate automaton (FSA) 208 and a collection of soft constraints 210. Atemporal coverage property includes a description for a sequence ofevents which are to be covered by a verification input stimuli, and canbe any temporal coverage property which can be converted into adeterministic finite state automaton (DFSA). In one embodiment of thepresent invention, a temporal coverage property can be in the form of aregular expression. A finite state automaton (FSA) includes a graph G(V,E) consisting of vertices V and edges E, wherein the vertices describethe allowable states of the respective temporal coverage property, andthe edges describe the conditions which allow a state transition from afirst state onto a second connected state. Furthermore, the FSA cancomprise an initial state and an accepting state, where thecorresponding temporal coverage property is marked as satisfied when theFSA reaches the accepting state. A soft constraint 210 includes a numberof Boolean conditions which allow an FSA 208 to navigate away from acorresponding active state and toward covering a corresponding temporalproperty. An instance of an FSA also includes local weight variableswhich are used to compute a probability that a corresponding softconstraint can be used to affect the selection of the inputs to the DUV.

Testbench 212 accepts DUV 214, and accepts FSA 208 and the correspondingsoft constraints 210 as input to generate input stimuli 218 for temporalcoverage property 202. Testbench 212 creates FSA instances duringsimulation to observe the inputs and outputs of the circuit design, andto determine the soft constraints which can satisfy a respectivetemporal coverage property. Testbench 212 uses soft constraints 210 tobias the random selection of the input stimuli based on a current stateof the FSA instance. A number of FSA instances propose a soft constraintat a respective time frame in the simulation, but those with higherweight values propose a corresponding soft constraint with a higherchance of affecting the selection of the inputs to the DUV.

In one embodiment of the present invention, testbench 212 is generatedby a user as an input to a circuit simulator. In another embodiment ofthe present invention, testbench 212 is automatically generated for theDUV as an input to a circuit simulator. In yet another embodiment of thepresent invention, testbench 212 is implemented as a software componentwhich interacts with a circuit simulator.

Monitoring mechanism 216 accepts temporal coverage property database 204and DUV 214 as input, and interacts with testbench 212 to monitor theinputs and outputs of DUV 214 and determine the temporal coverageproperties of temporal coverage property database 204 which have beencovered by input stimuli 218. In one embodiment of the presentinvention, testbench 212 includes monitoring mechanism 216.

Conversion Mechanism

FIG. 3 illustrates an exemplary usage of a conversion mechanism 304 inaccordance with an embodiment of the present invention. Conversionmechanism 304 accepts a temporal coverage property as input and producesa corresponding finite state automaton and soft constraints as output.The example illustrated in FIG. 3 presents an exemplary temporalcoverage property 302 which describes the allowable transactions for aread-modify-write instruction. This example uses the cover property andconstraint syntax of SystemVerilog, however, any other formalism can beused. Temporal coverage property 302 begins on an input vector where therequest signal “r” is set and the command signal is set to read(cmd==‘READ) for a number of clock cycles, such that a store addressvariable “v” is set to the value of the address bus “addr” (v=addr).This transaction can be followed by a number of input vectors where therequest signal “r” is not being set (!r[*0:$]). Finally, temporalcoverage property 302 is satisfied when the first two properties arefollowed by an input vector where the request signal “r” is set and thecommand signal is set to write (cmd==′WRITE) such that the address bus“addr” is set to the same value used by the read transaction of thetemporal coverage property “v” (v==addr).

The FSA produced by conversion mechanism 304 includes a number of statesand a number of transitions. The states of an FSA include a start state,from which the FSA begins analyzing a temporal coverage property, andincludes an accepting state, which can only be reached when the temporalcoverage property is satisfied by a given input sequence. The outgoingtransition condition of a start state describes the starting conditionof a temporal coverage property, and the incoming transition of anaccepting state describes the final condition for satisfying a temporalcoverage property. The intermediate states of an FSA and the transitionconditions between them describe the possible sequences of input vectorswhich can lead to satisfying the temporal coverage property.

When an FSA is instantiated during the simulation of the DUV, thetransition condition of an outgoing transition from the start state iscomputed against the input vector values of the DUV as soon as thepositive edge of the clock signal is encountered. If the transitioncondition is satisfied by the current values from the simulation, theFSA is advanced to the target state of the transition. Otherwise, if thetransition condition is not satisfied by the current values from thesimulation, the FSA instance is destroyed. A similar analysis isperformed by the FSA instance at the internal states as the FSA instancetransitions toward the accepting state. If the current input sequencesatisfies the temporal coverage property, the FSA instance will reachthe accepting state. Otherwise, if the current input sequence does notsatisfy the temporal coverage property, the FSA instance will bedestroyed and thus will not influence the stimulus generation anyfurther.

With respect to temporal coverage property 302, conversion mechanism 304creates FSA 306 with a start state 310 and an accepting state 320. WhenFSA 306 is instantiated during the simulation of the DUV, transitioncondition 312 is computed against the input vector values of the DUV assoon as the positive edge of the clock signal is encountered. Iftransition condition 312 is satisfied by the current values from thesimulation, the instance of FSA 306 is advanced to state S₁. Otherwise,if transition condition 312 is not satisfied by the current values fromthe simulation, the instance of FSA 306 is destroyed. A similar analysisis performed by the instance of FSA 306 at the internal states as theFSA instance transitions across state S₁ toward accepting state 320. Ifthe current input sequence satisfies temporal coverage property 302, theinstance of FSA 306 will reach accepting state 320. Otherwise, if thecurrent input sequence does not satisfy temporal coverage property 302,the instance of FSA 306 will be destroyed before it reaches acceptingstate 320.

The soft constraints 308 produced by conversion mechanism 304 include anumber of soft constraints wherein a respective soft constraint includesthe union of the transition conditions of the outgoing transitions froma corresponding state in the FSA. With respect to FSA 306, conversionmechanism 304 creates soft constraint C0 for state S₀ from thetransition condition of transition 314, and creates soft constraint C1for state S₁ from the union of the transition conditions for transitions316 and 318.

A number of FSA instances are simultaneously monitoring the inputvectors of the circuit simulation to attempt to bias the input vectortoward reaching a corresponding accepting state. As the simulation timeprogresses, a new FSA instance will have a lower likelihood of reachinga corresponding accepting state than an FSA instance which has beenactive for a given period of time. Therefore, it is important to gaugethe probability that an FSA instance will bias the input stimuli towarda corresponding state, and gauge the probability that the softconstraint of the FSA instance will evaluate to true or false.

The variables w0 and w1 of a soft constraint are weights used to proposea probability that the Boolean expression will evaluate to zero or one.These variables are local to an FSA instance, thus the weight values setby one FSA instance do not interfere with the weight values set byanother FSA instance. The weights w0 and w1 impose an approximateprobability w1/(w1+w0) that the Boolean expression will evaluate to onefor a corresponding FSA instance, and impose an approximate probabilityw0/(w0+w1) that the Boolean expression will evaluate to zero. Therefore,the weights w0 and w1 are used to provide a soft constraintcorresponding to an FSA instance with a probability of being selected tobias the input vectors at a respective time instance of the circuitsimulation.

The variable w1 of an FSA instance is set to a value w10 uponinstantiation, and is increased by a value Dw whenever the FSA instancetraverses a transition of the FSA graph. Incrementing w1 of an FSAinstance by Dw effectively increases the calculated value for theapproximate probability that the soft constraint evaluates to one, andreflects the increased probability that the FSA instance will reach theaccepting state. In other words, the weight w1 of an FSA instance isincreasing in value as the FSA instance progresses in time toward theaccepting state. This ensures the FSA instances which are closer toreaching the accepting state have a higher likelihood of biasing theinput stimuli than FSA instances which are farther from an acceptingstate. With respect to FSA 306, the weight w1 is increased by Dwwhenever the FSA instance reaches state S₁ (e.g., through transition316).

Constrained Random Simulation

FIG. 4 presents a flowchart illustrating the steps involved ingenerating input vectors to achieve coverage closure in accordance withan embodiment of the present invention. Before the system generatesbiased random input vectors, the system begins by performing asimulation on the circuit design with unbiased random input vectors(operation 401). Once the non-biased simulation is complete, the systemperforms a subsequent simulation which biases the random input stimulito satisfy the temporal coverage properties which were not coveredduring the non-biased simulation.

To generate biased random input vectors, the system first receives acollection of temporal coverage properties (operation 402), and selectsan uncovered temporal coverage property (operation 404). Next, thesystem generates input vectors for the selected temporal coverageproperty (operation 406). The system then determines if uncoveredtemporal coverage properties remain (operation 408). If so, the systemreturns to operation 404 to select another uncovered temporal coverageproperty. Otherwise if no more uncovered temporal coverage propertiesremain, then coverage closure has been achieved and the system returns acoverage report (operation 410).

FIG. 5 presents a flowchart illustrating the steps involved ingenerating input vectors for a temporal coverage property in accordancewith an embodiment of the present invention (operation 500). The systemfirst converts the temporal coverage property to an FSA and acorresponding collection of soft constraints (operation 502). Next, thesystem creates a new FSA instance for the current simulation time frame(operation 504). The system then generates random inputs for the nexttime frame (operation 506) by selecting a soft constraint from thecollection of FSA instances to bias the random input vectors, and thesystem advances the simulation time (operation 508). The system thendetermines if a respective FSA instance is at the accepting state(operation 510). If no FSA instance is at the accepting state, thesystem returns to operation 504 to generate input vectors for the nextsimulation time frame. Otherwise, if an FSA instance is at the acceptingstate, the system marks the temporal coverage property as satisfied(operation 512).

FIG. 6 presents a flowchart illustrating the steps involved ingenerating random inputs for a subsequent time frame in accordance withan embodiment of the present invention (operation 600). The system firstprunes the FSA instance collection (operation 602) to destroy the FSAinstances which cannot reach the accepting state. Next, the systemupdates the w1 weight for the remaining soft constraints by incrementingw1 by Dw (operation 606). Finally, the system generates the next set ofrandom inputs with values which are biased by a number of remaining softconstraints (operation 608). In one embodiment of the present invention,the system selects one soft constraint to bias the selection of theinputs to the DUV by selecting the FSA instance with the highest weight.In other embodiments, the system uses a collection of soft constraintssimultaneously, wherein the soft constraints which correspond to FSAinstances with higher weights have a greater influence on the selectionof the inputs to the DUV. Using the soft constraints to bias the inputstimuli involves biasing the random selection of the input stimuli basedon the current state of the FSA instances, wherein the random valuesgenerated for the inputs have a higher probability to conform to therequirements in the soft constraint with the highest w1 weight.

FIG. 7 presents a flowchart illustrating the steps involved in advancingthe simulation time in accordance with an embodiment of the presentinvention (operation 700). First, the system applies the generated inputvectors to the inputs of the circuit design under verification(operation 702). Next, the system advances the simulation time by oneclock cycle (operation 704) and evaluates the current state for the FSAinstances (operation 706).

FIG. 8 presents a flowchart illustrating the steps involved in pruningthe FSA instance collection in accordance with an embodiment of thepresent invention (operation 800). To prune the FSA instance collection,the system first selects an FSA instance to evaluate (operation 802) andthen evaluates the soft constraint expression for the active state ofthe FSA instance (operation 804). Next, the system determines if thesoft constraint expression evaluates to false. If so, the systemdestroys the FSA instance (operation 808). Otherwise, if the softconstraint expression does not evaluate to false, the FSA instance isretained in the collection (operation 809). Finally, the systemdetermines if there are more FSA instances to evaluate (operation 810).If an FSA instance which has not been evaluated remains, the systemreturns to operation 802 to select and evaluate an FSA instance whichhas not yet been evaluated.

The foregoing descriptions of embodiments of the present invention havebeen presented only for purposes of illustration and description. Theyare not intended to be exhaustive or to limit the present invention tothe forms disclosed. Accordingly, many modifications and variations willbe apparent to practitioners skilled in the art. Additionally, the abovedisclosure is not intended to limit the present invention. The scope ofthe present invention is defined by the appended claims.

What is claimed is:
 1. A method for verifying a circuit which biasesinput stimuli for the circuit to satisfy one or more temporal coverageproperties to be verified for the circuit, comprising: performing, byusing one or more computers, a simulation in which random input stimuliare applied to the circuit; wherein said performing the simulationinvolves, using a finite state automaton (FSA) instance for a temporalcoverage property to observe inputs and outputs of the circuit, whereinthe FSA instance includes a sequence of states which satisfy thetemporal coverage property, and wherein using the FSA instance involvesconcurrently using multiple FSA instances associated with one or moretemporal coverage properties to observe the inputs and outputs of thecircuit; and using soft constraints associated with the FSA instance tobias input stimuli for the circuit so that the simulation is likely toprogress through the sequence of states.
 2. The method of claim 1,wherein said using soft constraints to bias the input stimuli for thecircuit involves biasing a random selection of the input stimuli basedon a current state of the FSA instance.
 3. The method of claim 1,wherein if the FSA instance reaches an accepting state for the temporalcoverage property, the method further comprises marking the temporalcoverage property as satisfied.
 4. The method of claim 1, wherein saidbiasing the input stimuli for the circuit involves ensuring that FSAinstances which are closer to an accepting state have a higherlikelihood of biasing the input stimuli than FSA instances which arefarther from an accepting state.
 5. The method of claim 1, wherein priorto performing the simulation, the method further comprises performing apreceding non-biased simulation in which the input stimuli to thecircuit are not biased to satisfy specific temporal coverage properties;and wherein after the preceding non-biased simulation is complete, thesimulation subsequently biases the input stimuli to cover temporalcoverage properties which were not covered during the precedingnon-biased simulation.
 6. The method of claim 1, wherein prior toperforming the simulation, the method further comprises converting thetemporal coverage property into the FSA instance and the softconstraints.
 7. The method of claim 6, wherein the FSA is adeterministic FSA.
 8. A circuit verification system which biases inputstimuli for a circuit to satisfy one or more temporal coverageproperties to be verified for the circuit, comprising: a conversionmechanism configured to convert a respective temporal coverage propertyinto a corresponding finite state automaton (FSA) and into acorresponding collection of soft constraints, wherein the correspondingcollection of soft constraints defines allowable transitions across theFSA; and a testbench environment configured to perform a simulation inwhich random input stimuli are applied to the circuit, wherein thetestbench environment performs the simulation by: using an FSA instancefor a temporal coverage property to observe inputs and outputs of thecircuit, wherein the FSA instance includes a sequence of states whichsatisfy the temporal coverage property, and wherein using the FSAinstance involves concurrently using multiple FSA instances associatedwith one or more temporal coverage properties to observe the inputs andoutputs of the circuit; and using soft constraints associated with theFSA instance to bias input stimuli for the circuit so that thesimulation is likely to progress through the sequence of states.
 9. Thecircuit verification system of claim 8, wherein said using softconstraints to bias the input stimuli for the circuit involves biasing arandom selection of the input stimuli based on a current state of theFSA instance.
 10. The circuit verification system of claim 8, wherein ifthe FSA instance reaches an accepting state for the temporal coverageproperty, the method further comprises marking the temporal coverageproperty as satisfied.
 11. The circuit verification system of claim 8,wherein said biasing the input stimuli for the circuit involves ensuringthat FSA instances which are closer to an accepting state have a higherlikelihood of biasing the input stimuli than FSA instances which arefarther from an accepting state.
 12. The circuit verification system ofclaim 8, wherein the FSA is a deterministic FSA.
 13. The circuitverification system of claim 8, wherein prior to performing thesimulation, the testbench environment is further configured to perform apreceding non-biased simulation in which the input stimuli to thecircuit are not biased to satisfy specific temporal coverage properties;and wherein after the preceding non-biased simulation is complete, thesimulation subsequently biases the input stimuli to cover temporalcoverage properties which were not covered during the precedingnon-biased simulation.
 14. The circuit verification system of claim 8,further comprising a monitoring mechanism configured to: monitor theinputs and outputs of the circuit, and during simulation of a giveninput stimuli, to determine the temporal coverage properties which havebeen covered by the input stimuli.
 15. A non-transitorycomputer-readable storage medium storing instructions that when executedby a computer cause the computer to perform a method for verifying acircuit which biases input stimuli for the circuit to satisfy one ormore temporal coverage properties to be verified for the circuit,comprising: performing a simulation in which random input stimuli areapplied to the circuit; wherein performing the simulation involves,using a finite state automaton (FSA) instance for a temporal coverageproperty to observe inputs and outputs of the circuit, wherein the FSAinstance includes a sequence of states which satisfy the temporalcoverage property, and wherein using the FSA instance involvesconcurrently using multiple FSA instances associated with one or moretemporal coverage properties to observe the inputs and outputs of thecircuit, and using soft constraints associated with the FSA instance tobias input stimuli for the circuit so that the simulation is likely toprogress through the sequence of states.
 16. The non-transitorycomputer-readable storage medium of claim 15, wherein said using softconstraints to bias the input stimuli for the circuit involves biasing arandom selection of the input stimuli based on a current state of theFSA instance.
 17. The non-transitory computer-readable storage medium ofclaim 15, wherein if the FSA instance reaches an accepting state for thetemporal coverage property, the method further comprises marking thetemporal coverage property as satisfied.
 18. The non-transitorycomputer-readable storage medium of claim 15, wherein said biasing theinput stimuli for the circuit involves ensuring that FSA instances whichare closer to an accepting state have a higher likelihood of biasing theinput stimuli than FSA instances which are farther from an acceptingstate.
 19. The non-transitory computer-readable storage medium of claim15, wherein prior to performing the simulation, the method furthercomprises performing a preceding non-biased simulation in which theinput stimuli to the circuit are not biased to satisfy specific temporalcoverage properties; and wherein after the preceding non-biasedsimulation is complete, the simulation subsequently biases the inputstimuli to cover temporal coverage properties which were not coveredduring the preceding non-biased simulation.
 20. The non-transitorycomputer-readable storage medium of claim 15, wherein prior toperforming the simulation, the method further comprises converting thetemporal coverage property into the FSA instance and the softconstraints.
 21. The non-transitory method of claim 20, wherein the FSAinstance is a deterministic FSA instance.